An asymptotically efficient ARMA estimator based on sample covariances

An asymptotically efficient autoregressive moving-average (ARMA) spectral estimator is presented, based on the sample covariances of observed time series. The estimate of the autoregressive (AR) part is shown to be identical to the optimal instrumental variable (IV) estimator in [7] although derived here using a different approach. The moving-average (MA) spectral parameter estimate is new.     

Designing Unimodular Sequence Sets With Good Correlations—Including an Application to MIMO Radar

A multiple-input multiple-output (MIMO) radar system that transmits orthogonal waveforms via its antennas can achieve a greatly increased virtual aperture compared with its phased-array counterpart. This increased virtual aperture enables many of the MIMO radar advantages, including enhanced parameter identifiability and improved resolution. Practical radar requirements such as unit peak-to-average power ratio and range compression dictate that we use MIMO radar waveforms that have constant modulus and good auto- and cross-correlation properties. We present in this paper new computationally efficient cyclic algorithms for MIMO radar waveform synthesis. These algorithms can be used for the design of unimodular MIMO sequences that have very low auto- and cross-correlation sidelobes in a specified lag interval, and of very long sequences that could hardly be handled by other algorithms previously suggested in the literature. A number of examples are provided to demonstrate the performances of the new waveform synthesis algorithms.     

Using prior knowledge in SVD-based parameter estimation for magnetic resonance spectroscopy--the ATP example

We introduce the knowledge-based singular value decomposition (KNOB-SVD) method for exploiting prior knowledge in magnetic resonance (MR) spectroscopy based on the SVD of the data matrix. More specifically, we assume that the MR data are well modeled by the superposition of a given number of exponentially damped sinusoidal components and that the dampings alphakappa, frequencies omegakappa, and complex amplitudes rhokappa of some components satisfy the following relations: alphakappa = alpha (alpha = unknown), omegakappa = omega + (kappa- 1)delta (omega = unknown, delta = known), and rhokappa = Ckapparho (rho = unknown, ckappa = known real constants). The adenosine triphosphate (ATP) complex, which has one triple peak and two double peaks whose dampings, frequencies, and amplitudes may in some cases be known to satisfy the above type of relations, is used as a vehicle for describing our SVD-based method throughout the paper. By means of numerical examples, we show that our method provides more accurate parameter estimates than a commonly used general-purpose SVD-based method and a previously suggested prior knowledge-based SVD method.     

System identification from noisy measurements by using instrumental variables and subspace fitting

This paper considers the estimation of the parameters of a linear discrete-time system from noise-perturbed input and output measurements. The conditions imposed on the system are fairly general. In particular, the input and output noises are allowed to be auto-correlated and they may be cross-correlated as well. The proposed method makes use of an instrumental variable (IV)-vector to produce a covariance matrix that is pre- and postmultiplied by some prechosen weights. The singular vectors of the so-obtained matrix possess complete information on the system parameters. A weighted subspace fitting (WSF) method is then applied to the aforementioned singular vectors to consistently estimate the parameters of the system. The IV-WSF technique suggested herein is noniterative and easy to implement, and has a small computational burden. The asymptotic distribution of its estimation errors is derived and the result is used to motivate the choice of the weighting matrix in the WSF step and also to predict the estimation accuracy. Numerical examples are included to illustrate the performance achievable by the method.This work has been supported by the Swedish Research Council of Engineering Sciences under contract 93-669 and by the Göran Gustafsson Foundation.     

Cisoid parameter estimation in the colored noise case: asymptoticCramer-Rao bound, maximum likelihood, and nonlinear least-squares

The problem of estimating the parameters of complex-valued sinusoidal signals (cisoids, for short) from data corrupted by colored noise occurs in many signal processing applications. We present a simple formula for the asymptotic (large-sample) Cramer-Rao bound (CRB) matrix associated with this problem. The maximum likelihood method (MLM), which estimates both the signal and noise parameters, attains the performance corresponding to the asymptotic CRB, as the sample length increases. More interestingly, we show that a computationally much simpler nonlinear least-squares method (NLSM), which estimates the signal parameters only, achieves the same performance in large samples     

Detection tests for array processing in unknown correlated noisefields

This paper introduces two eigenvalue-based rules for estimating the number of signals impinging on an array of sensors along with a spatially correlated noise field. The first rule, called S, is derived under the assumption that the noise spatial covariance is block diagonal or banded. The assumption underlying the second detection rule, named T, is that the temporal correlation of the noise has a shorter length than that of the signals. In both cases, a matrix is built from the array output data covariances, the smallest eigenvalue of which is equal to zero under the hypothesis that the source number is overestimated. The sample distribution of the aforementioned smallest eigenvalue is derived and used to formulate the detection rules S and T. Both these rules are computationally quite simple. Additionally, they can be used with a noncalibrated array. The paper includes numerical examples that lend empirical support to the theoretical findings and illustrate the kind of performance that can be achieved by using the S and T detection rules     

MODE-type algorithm for estimating damped, undamped, or explosive modes

We propose a new algorithm for estimating the parameters of damped, undamped, or explosive sinusoidal signals. The algorithm resembles the MODE algorithm, which is commonly used for direction of arrival estimation in the array signal processing field. It is derived as a natural approximation to an asymptotically (high-SNR) optimal parameter estimator and has excellent statistical accuracy. Nevertheless, it is computationally simple and easy to implement. Numerical examples are included to illustrate the performance of the proposed method.This work has been supported by the Swedish Research Council for Engineering Sciences (TFR).     

Exploiting arrays with multiple invariances using MUSIC and MODE

This paper describes several new techniques for direction of arrival (DOA) estimation using arrays composed of multiple translated and uncalibrated subarrays. The new algorithms can be thought of as generalizations of the MUSIC, Root-MUSIC, and MODE techniques originally developed for fully calibrated arrays. The advantage of these new approaches is that the DOAs can be estimated using either a simple one-dimensional (1-D) search or by rooting a polynomial, as opposed to the multidimensional search required by multiple invariance (MI)-ESPRIT. When it can be applied, the proposed MI-MODE algorithm shares the statistical optimality of MI-ESPRIT. While MI-MUSIC and Root-MI-MUSIC are only optimal for uncorrelated sources, they perform better than a single invariance implementation of ESPRIT and are thus better suited for finding the initial conditions required by the MI-ESPRIT search     

Si Doping of GaN in Hydride Vapor-Phase Epitaxy

Growth of GaN boules by hydride vapor-phase epitaxy (HVPE) is very attractive for fabrication of GaN substrates. Use of dichlorosilane as a source for Si doping of bulk GaN is investigated. It is shown that no tensile strain is incorporated into mm-thick, Si-doped GaN layers on sapphire substrates if the threading dislocation density is previously reduced to 2.5 × 10⁷ cm^?2 or below. High-quality GaN layers with electron densities up to 1.5 × 10¹⁹ cm^?3 have been achieved, and an upper limit of about 4 × 10¹⁹ cm^?3 for Si doping of GaN boules was deduced considering the evolution of dislocations with thickness. A 2-inch, Si-doped GaN crystal with length exceeding 6 mm and targeted Si doping of about 1 × 10¹⁸ cm^?3 is demonstrated.     

Securing User-Controlled Routing Infrastructures

Designing infrastructures that give untrusted third parties (such as end-hosts) control over routing is a promising research direction for achieving flexible and efficient communication. However, serious concerns remain over the deployment of such infrastructures, particularly the new security vulnerabilities they introduce. The flexible control plane of these infrastructures can be exploited to launch many types of powerful attacks with little effort. In this paper, we make several contributions towards studying security issues in forwarding infrastructures (FIs). We present a general model for an FI, analyze potential security vulnerabilities, and present techniques to address these vulnerabilities. The main technique that we introduce in this paper is the use of simple lightweight cryptographic constraints on forwarding entries. We show that it is possible to prevent a large class of attacks on end-hosts and bound the flooding attacks that can be launched on the infrastructure nodes to a small constant value. Our mechanisms are general and apply to a variety of earlier proposals such as , DataRouter, and Network Pointers.